• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3716-3724
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• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.3
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• pp.229-236
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• 2020

A crush switch assembly(CSA) connected to an impact fuze provides electrical signal for detonation of the loaded main charge when an impact with the target is detected. Because the CSA experiences continuous changes in flight environment such as changes in velocity, vibration, and stresses, it is necessary to accurately predict the behavior of the fuze to maintain functionality during flight and to detonate when necessary. In this paper, random vibration analysis for flight environment and impact analysis on target hit are performed using FEA. Then, high speed impact tests are performed with the original and scaled down models to ensure operation validation of the manufactured products. The test results are then compared with M&S results to verify the capability of currently modeled CSA.

• Proceedings of the Korean Magnestics Society Conference
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• 2007.05a
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• pp.35.1-35.1
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• 2007

• Proceedings of the KSMRM Conference
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• 2006.11a
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• pp.36-36
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• 2006

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2567-2582
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• 2016

This paper investigates the impact of outdated channel state information (CSI) on the performance of the incremental amplify-and-forward (AF) opportunistic relaying (OR) over dual-hop Rayleigh fading channels. According to the definition of distribution function, we obtain the cumulative distribution function (CDF) of the actual combined signal-to-noise ratio (SNR) received at the destination. Based on this CDF, the closed-form expressions of the average spectral efficiency and outage probability are derived for the incremental AF OR under outdated CSI. Numerical results show that in the low region of the average SNR of the direct link, outdated CSI deteriorates the system performance, whereas in the high region, outdated CSI has almost no impact on the system performance.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.2
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• pp.337-343
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• 2012

This work investigates the vibrational characteristics of the underground gas pipelines. Experiments were conducted to analyze the effects of various parameters on the vibrational characteristics from the emergency detection point of view. Influences of the various types of impact exerted on the pipe, height of free fall and measuring locations were analyzed. Especially, the difference between the vibrational signal generated by the direct impact on the pipe and the ambient noise was successfully identified. To validate the experimental observation, computer simulation was also performed with constant properties(elasticity, fluid velocity and internal pressure) which are directly conjectured from the accompanying experiment with a real pipe system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.522-522
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• 2000

In this paper, two pre-filtering techniques are presented for accurately estimating the impact location of a loose part. The reason why a pre-filterng technique Is necessary in a Loose Part Monitoring System is that the effects of background noise on the signal to noise ratio (SNR) can be reduced considerably resulting in improved estimation accuracy. The first method is to take d moving average operation in the time domain. The second one is to adopt band-pass filters designed in the frequency domain such as a Butterworth filter, Chebyshev filter I & II and an Elliptic Filter. To show the effectiveness, the impact test data (signals) from the YGN3 power plant are first preprocessed and then used to estimate the loose pan impact position. Resultantly. we observed that SNR is much improved and the average estimation error is below 7.5%.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.318-322
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• 2019

In shooting training, an impact point identification system that uses the impact wave of the bullet to check the impact point in the target plate has been recently used. Acoustic sensors used in these systems must be able to detect shock waves of high sound pressure levels and be both waterproof and dustproof for rainy weather and dusty environments, respectively. In this study, membranes with excellent waterproof, dustproof, and sound transmitting characteristics were selected through a characteristics test; a protection cap was installed to install the selected materials. After coupling the produced protection cap to the acoustic sensor housing, the sensitivity and phase characteristics of the acoustic sensor were checked. Through the waterproof and dustproof test, the performances of its sensitivity and phase characteristics were confirmed. Finally, the normal shockwave of a 5.56 mm diameter bullet was measured using a shockwave detection signal collecting plate equipped with a prototype of the acoustic sensor at a 100 m firing range.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.42-48
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• 1999

A control method for a robot hand grasping a object in a partially unknown environment will be proposed, where a proximate sensor detecting the distance between the fingertip and object was used. Particularly, the finger joints were driven servo-pneumatically in this study. Based on the proximate sensor signal the finger motion controller could plan the grasping process divided in three phases ; fast aproach, slow transitional contact and contact force control. That is, the fingertip approached to the object with full speed, until the output signal of the proximate sensor began to change. Within the perating range of the proximate sensor, the finger joint was moved by a state-variable feedback position controller in order to obtain a smooth contact with the object. The contact force of fingertip was then controlled using the blocked-line pressure sensitivity of the flow control servovalve for finger joint control. In this way, the grasping impact could be reduced without reducing the object approaching speed. The performance of the proposed grasping method was experimentally compared with that of a open loop-controlled one.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1204-1208
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• 2011

In this study, we proposed the system that calculates the algorithm with an accelerometer signal and detects the fall shock and it's direction. In order to gather the activity patterns of fall status and attach on the subject's body without consciousness, the device needs to be small. With this aim, it is attached on the right side of subject's waist. With roll and pitch angle which represent the activity of upper body, the fall situation is determined and classified into the posture pattern. The impact is calculated by the vector magnitude of accelerometer signal. And in the case of the elderly keep the same posture after fall, it can distinguish the situation whether they can stand by themselves or not. Our experimental results showed that 95% successful detection rate of fall activity with 10 subjects. For further improvement of our system, it is necessary to include tasks-oriented classifying algorithm to diverse fall conditions.